Machine Learning pipelines for optimizing online marketing performance

Project Description

We would like to create ML pipelines to improve the conversion performance (leads and sales) of the ads we manage on adwords, facebook ads, instagram, twitter ads.

We're looking for a long-term engagement with someone who ideally has some experience with applied ML in digital advertising.

About us

We're a digital advertising management company for SMBs. We launched in April 2016 and currently have ~150 active customers.

Project

Overall, we're trying to improve the conversion performance of our customer's campaigns in an automed way. We believe in order to do this, we need to start by using ML pipelines to output suggested values for % of budget being allocated to the different channels (see above). We also believe there are other pieces to this puzzle but we want to start with the channel allocation suggestions and then move on from there.

We already have a team of developers that will performing any of the devops needed for this project.

So we're looking for a someone to help us do the following:

• develop models in R or python using past experience and our data
• help us develop the proper techniques to utilize the model pipelines

We plan on using AzureML to construct our pipelines/APIs. You do not need to know AzureML, you can pick it up along the way as we work together to implement the pipeline(s) you construct.

Data

The data will be advertising performance data from adwords, facebook ads, instagram, twitter ads as well as web site and conversion analytics data.

This data will be ETL'd by us and made available to you in DBs to conduct your work.

However, ideally you have a method to access the APIs directly (ex. Pentaho) as well while performing your function to streamline the workflow (example you need access to something that we're not currently capturing from the APIs)... so that would be ideal, not required.